// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#include "pooling.h"
#include <float.h>
#include <algorithm>

namespace ncnn {

    DEFINE_LAYER_CREATOR(Pooling)

    Pooling::Pooling() {
        one_blob_only = true;
        support_inplace = false;
    }

    int Pooling::load_param(const ParamDict &pd) {
        pooling_type = pd.get(0, 0);
        kernel_w = pd.get(1, 0);
        kernel_h = pd.get(11, kernel_w);
        stride_w = pd.get(2, 1);
        stride_h = pd.get(12, stride_w);
        pad_left = pd.get(3, 0);
        pad_right = pd.get(14, pad_left);
        pad_top = pd.get(13, pad_left);
        pad_bottom = pd.get(15, pad_top);
        global_pooling = pd.get(4, 0);
        pad_mode = pd.get(5, 0);

        return 0;
    }

    int Pooling::forward(const Mat &bottom_blob, Mat &top_blob) const {
        // max value in NxN window
        // avg value in NxN window

        int w = bottom_blob.w;
        int h = bottom_blob.h;
        int channels = bottom_blob.c;

//     fprintf(stderr, "Pooling     input %d x %d  pad = %d %d  ksize=%d %d  stride=%d %d\n", w, h, pad_w, pad_h, kernel_w, kernel_h, stride_w, stride_h);
        if (global_pooling) {
            top_blob.create(channels);
            if (top_blob.empty())
                return -100;

            int size = w * h;

            if (pooling_type == PoolMethod_MAX) {
#pragma omp parallel for
                for (int q = 0; q < channels; q++) {
                    const float *ptr = bottom_blob.channel(q);

                    float max = ptr[0];
                    for (int i = 0; i < size; i++) {
                        max = std::max(max, ptr[i]);
                    }

                    top_blob[q] = max;
                }
            } else if (pooling_type == PoolMethod_AVE) {
#pragma omp parallel for
                for (int q = 0; q < channels; q++) {
                    const float *ptr = bottom_blob.channel(q);

                    float sum = 0.f;
                    for (int i = 0; i < size; i++) {
                        sum += ptr[i];
                    }

                    top_blob[q] = sum / size;
                }
            }

            return 0;
        }

        Mat bottom_blob_bordered = bottom_blob;

        float pad_value = 0.f;
        if (pooling_type == PoolMethod_MAX) {
            pad_value = -FLT_MAX;
        } else if (pooling_type == PoolMethod_AVE) {
            pad_value = 0.f;
        }

        int wtailpad = 0;
        int htailpad = 0;

        if (pad_mode == 0) // full padding
        {
            int wtail = (w + pad_left + pad_right - kernel_w) % stride_w;
            int htail = (h + pad_top + pad_bottom - kernel_h) % stride_h;

            if (wtail != 0)
                wtailpad = stride_w - wtail;
            if (htail != 0)
                htailpad = stride_h - htail;

            copy_make_border(bottom_blob, bottom_blob_bordered, pad_top, pad_bottom + htailpad, pad_left,
                             pad_right + wtailpad, BORDER_CONSTANT, pad_value);
            if (bottom_blob_bordered.empty())
                return -100;

            w = bottom_blob_bordered.w;
            h = bottom_blob_bordered.h;
        } else if (pad_mode == 1) // valid padding
        {
            copy_make_border(bottom_blob, bottom_blob_bordered, pad_top, pad_bottom, pad_left, pad_right,
                             BORDER_CONSTANT, pad_value);
            if (bottom_blob_bordered.empty())
                return -100;

            w = bottom_blob_bordered.w;
            h = bottom_blob_bordered.h;
        } else if (pad_mode == 2) // tensorflow padding=SAME
        {
            int wpad = kernel_w + (w - 1) / stride_w * stride_w - w;
            int hpad = kernel_h + (h - 1) / stride_h * stride_h - h;
            if (wpad > 0 || hpad > 0) {
                copy_make_border(bottom_blob, bottom_blob_bordered, hpad / 2, hpad - hpad / 2, wpad / 2,
                                 wpad - wpad / 2, BORDER_CONSTANT, pad_value);
                if (bottom_blob_bordered.empty())
                    return -100;
            }

            w = bottom_blob_bordered.w;
            h = bottom_blob_bordered.h;
        }

        int outw = (w - kernel_w) / stride_w + 1;
        int outh = (h - kernel_h) / stride_h + 1;

        top_blob.create(outw, outh, channels);
        if (top_blob.empty())
            return -100;

        const int maxk = kernel_w * kernel_h;

        // kernel offsets
        std::vector<int> _space_ofs(maxk);
        int *space_ofs = &_space_ofs[0];
        {
            int p1 = 0;
            int p2 = 0;
            int gap = w - kernel_w;
            for (int i = 0; i < kernel_h; i++) {
                for (int j = 0; j < kernel_w; j++) {
                    space_ofs[p1] = p2;
                    p1++;
                    p2++;
                }
                p2 += gap;
            }
        }

        if (pooling_type == PoolMethod_MAX) {
#pragma omp parallel for
            for (int q = 0; q < channels; q++) {
                const Mat m = bottom_blob_bordered.channel(q);
                float *outptr = top_blob.channel(q);

                for (int i = 0; i < outh; i++) {
                    for (int j = 0; j < outw; j++) {
                        const float *sptr = m.row(i * stride_h) + j * stride_w;

                        float max = sptr[0];

                        for (int k = 0; k < maxk; k++) {
                            float val = sptr[space_ofs[k]];
                            max = std::max(max, val);
                        }

                        outptr[j] = max;
                    }

                    outptr += outw;
                }
            }
        } else if (pooling_type == PoolMethod_AVE) {
#pragma omp parallel for
            for (int q = 0; q < channels; q++) {
                const Mat m = bottom_blob_bordered.channel(q);
                float *outptr = top_blob.channel(q);

                for (int i = 0; i < outh; i++) {
                    for (int j = 0; j < outw; j++) {
                        const float *sptr = m.row(i * stride_h) + j * stride_w;

                        float sum = 0;

                        for (int k = 0; k < maxk; k++) {
                            float val = sptr[space_ofs[k]];
                            sum += val;
                        }

                        outptr[j] = sum / maxk;
                    }

                    outptr += outw;
                }

                // fix pad
                if (pad_top != 0) {
                    const float scale = (float) kernel_h / (kernel_h - pad_top);

                    outptr = top_blob.channel(q).row(0);
                    for (int i = 0; i < outw; i++) {
                        outptr[i] *= scale;
                    }
                }
                if (pad_bottom + htailpad != 0) {
                    const float scale = (float) kernel_h / (kernel_h - pad_bottom - htailpad);

                    outptr = top_blob.channel(q).row(outh - 1);
                    for (int i = 0; i < outw; i++) {
                        outptr[i] *= scale;
                    }
                }
                if (pad_left != 0) {
                    const float scale = (float) kernel_w / (kernel_w - pad_left);

                    outptr = top_blob.channel(q);
                    for (int i = 0; i < outh; i++) {
                        *outptr *= scale;
                        outptr += outw;
                    }
                }
                if (pad_right + wtailpad != 0) {
                    const float scale = (float) kernel_w / (kernel_w - pad_right - wtailpad);

                    outptr = top_blob.channel(q);
                    outptr += outw - 1;
                    for (int i = 0; i < outh; i++) {
                        *outptr *= scale;
                        outptr += outw;
                    }
                }
            }
        }

        return 0;
    }

} // namespace ncnn
